class_docstr: Train a Pytorch ConvNet with Trainable and PopulationBasedTraining
  scheduler. The example reuse some of the functions in mnist_pytorch,
  and is a good demo for how to add the tuning function without
  changing the original training code.
class_docstr_tok: ["Train","a","Pytorch","ConvNet","with","Trainable","and","PopulationBasedTraining","scheduler",".","The","example","reuse","some","of","the","functions","in","mnist_pytorch",",","and","is","a","good","demo","for","how","to","add","the","tuning","function","without","changing","the","original","training","code","."]
code: def reset_config(self, new_config):
      for param_group in self.optimizer.param_groups:
          if "lr" in new_config:
              param_group["lr"] = new_config["lr"]
          if "momentum" in new_config:
              param_group["momentum"] = new_config["momentum"]
  
      self.config = new_config
      return True
  
file_path: ray/python/ray/tune/examples/pbt_convnet_example.py
imports: import argparse
  import os
  import numpy
  import torch
  import torch.optim
  from torchvision import datasets
  from ray.tune.examples.mnist_pytorch import train_func, test_func, ConvNet, get_data_loaders
  import ray
  from ray import train, tune
  from ray.tune.schedulers import PopulationBasedTraining
  from ray.tune.utils import validate_save_restore
label_desc: Use image node_id 5 for calling the PytorchTrainable obj's underlying member method code with example usage: obj.reset_config(new_config) and returns: True